• Written by Muhammed Abdulla NC, CEO

A meeting to discuss the return on investment of a new supply chain intervention can quickly become mired in uncertainty, as executives struggle to isolate the impact of the change from the many other factors that influence supply chain performance. This is because the counterfactual - what would have happened if the intervention had not been made - is invisible, making it difficult to measure the true effect of the change. In this situation, it is tempting to rely on aggregate outcome metrics, such as total inventory cost or average lead time, to evaluate the success of the intervention. However, this approach is flawed, as these metrics can be influenced by a wide range of factors unrelated to the intervention.

Leading measures you can actually observe

Instead of relying on aggregate outcome metrics, it is more effective to focus on leading measures that can be directly observed. For example, a shift supervisor can check the maintenance log to see if equipment is being properly maintained, or a category manager can compare the performance of different suppliers. By tracking these leading measures, it is possible to get a clearer picture of the impact of the intervention, and to make adjustments as needed. This approach also allows for more timely intervention, as problems can be identified and addressed before they have a major impact on supply chain performance.

Most people get wrong about this that they think aggregate metrics are enough to measure the success of an intervention, but they are wrong. Aggregate metrics can be misleading, and leading measures are essential to get a true picture of the impact of the intervention.

Why aggregate outcome metrics move for unrelated reasons

Aggregate outcome metrics, such as total inventory cost or average lead time, can be influenced by a wide range of factors unrelated to the intervention. For example, changes in demand or supplier performance can impact these metrics, making it difficult to determine the true effect of the intervention. Additionally, these metrics can be affected by seasonal fluctuations or other external factors, further complicating the analysis. By relying solely on these metrics, executives may be misled into thinking that the intervention is having a greater or lesser impact than it actually is.

This is particularly problematic in supply chain management, where the complexity of the system and the many interacting variables make it difficult to isolate the impact of a single intervention. As a result, it is essential to use more nuanced and targeted metrics to evaluate the success of a supply chain intervention.

The supply chain data this depends on

The ability to measure the impact of a supply chain intervention depends on having access to high-quality data on purchase and delivery history, lead times, inventory by location, and demand signal. This data can be used to track leading measures, such as the percentage of orders filled on time, or the average inventory turnover. By analyzing this data, executives can gain a clearer understanding of the impact of the intervention, and make more informed decisions about how to optimize supply chain performance. For more information on how to optimize supply chain operations, visit our operations page.

Having this data also allows executives to identify areas where the intervention is having the greatest impact, and to adjust their strategy accordingly. For example, if the data shows that the intervention is having a significant impact on lead time variance, but not on inventory levels, executives may choose to focus their efforts on further reducing lead time variance.

What it changes about

The use of leading measures and targeted metrics can reveal hidden problems in the supply chain, such as lead time variance that is hidden behind a stable average. This can occur when some orders are consistently filled quickly, while others are delayed, resulting in a stable average lead time that masks the underlying variability. By tracking lead time variance, executives can identify and address these problems, resulting in more reliable and efficient supply chain performance.

Similarly, the use of targeted metrics can help executives to identify inventory that is being held against the wrong risk. For example, if inventory is being held to protect against a risk that is no longer relevant, executives can adjust their inventory management strategy to better align with current risks and demand patterns.

Measured with

The impact of a supply chain intervention can be measured using a variety of metrics, including lead time variance, fill rate, and inventory turns. These metrics provide a more nuanced and targeted view of supply chain performance, and can help executives to identify areas where the intervention is having the greatest impact. By tracking these metrics, executives can make more informed decisions about how to optimize supply chain performance, and can adjust their strategy to better achieve their goals.

For example, a category manager can use these metrics to compare the performance of different suppliers, and to identify areas where the supplier is not meeting expectations. This can help to improve supplier performance, and to reduce the risk of supply chain disruptions.

What this does not do

This approach to measuring the impact of a supply chain intervention does not provide a complete picture of the overall performance of the supply chain. It is focused specifically on the impact of the intervention, and does not take into account other factors that may be influencing supply chain performance. As a result, it is essential to use this approach in conjunction with other metrics and analysis techniques to get a comprehensive view of the supply chain. If you have questions about how to implement this approach, or would like to learn more about how AsscherAi can help you to optimize your supply chain, visit our contact page.